The present invention relates to user recognition systems and methods and, more particularly, to systems and methods for access filtering employing relaxed recognition constraints.
It is known that user recognition systems are systems which employ recognition techniques in order to attempt to accurately identify and/or verify the identity of a particular user so that, for example, the user's request for access to a service and/or facility (or device) may be accepted or rejected based on the results of such identification/verification (recognition). Conventional user recognition systems try to equally minimize both a false acceptance rate and a false rejection rate. In other words, such systems typically aim at pushing down the control curve of the system in order to simultaneously reduce both false acceptance and false rejection. However, inevitably, the emphasis is systematically placed on substantially reducing the false acceptance rate, while maintaining an otherwise acceptable false rejection rate. This is typically the case with respect to security systems, for which prevention of non-authorized intrusions, i.e., false acceptances, is of paramount concern. That is, such systems tolerate false rejections, while attempting to permit no false acceptances.
One example of a user recognition technique is speaker recognition. Speaker recognition (identification/verification) can be done in text-dependent or text-prompted mode (where the text of an utterance is prompted by the speech recognizer and recognition depends on the accuracy of the words uttered as compared to the prompted text), or text-independent mode (where the utterances of the speaker are used to perform recognition by comparing the acoustic characteristics of the speaker with acoustic models of previously enrolled speakers, irrespective of the words uttered). Regardless of the mode employed, speaker recognition usually involves the comparison of the utterance with a claimed speaker model. A measure of the match between model and utterance is thereafter compared to a similar measure obtained over competing models, for instance, cohort or background models. Cohorts are composed of previously enrolled speakers who possess voice (acoustic) characteristics that are substantially similar, i.e., closest, to the speaker who tries to access the service and/or facility. Cohort models are the acoustic models built from acoustic features respectively associated with the cohort speakers. A background model is an average model built from acoustic features over the global population.
Accordingly, regardless of the user recognition technique employed and, with respect to speaker recognition, regardless of whether or not text-dependent, text-independent or any other type of speaker recognition is performed using cohort models, background models or the like, it would be desirable and highly advantageous to provide systems and methods for filtering access to a service/facility which substantially eliminate false rejections while providing a reasonable false acceptance rate.